Data 608 Assignment 1

Jeyaraman Ramalingam

Principles of Data Visualization and Introduction to ggplot2

I have provided you with data about the 5,000 fastest growing companies in the US, as compiled by Inc. magazine. lets read this in:

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.0.6     v dplyr   1.0.4
## v tidyr   1.1.2     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(dplyr)
library(DT)

inc <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv", header= TRUE)

And lets preview this data:

head(inc)
summary(inc)
##       Rank          Name            Growth_Rate         Revenue         
##  Min.   :   1   Length:5001        Min.   :  0.340   Min.   :2.000e+06  
##  1st Qu.:1252   Class :character   1st Qu.:  0.770   1st Qu.:5.100e+06  
##  Median :2502   Mode  :character   Median :  1.420   Median :1.090e+07  
##  Mean   :2502                      Mean   :  4.612   Mean   :4.822e+07  
##  3rd Qu.:3751                      3rd Qu.:  3.290   3rd Qu.:2.860e+07  
##  Max.   :5000                      Max.   :421.480   Max.   :1.010e+10  
##                                                                         
##    Industry           Employees           City              State          
##  Length:5001        Min.   :    1.0   Length:5001        Length:5001       
##  Class :character   1st Qu.:   25.0   Class :character   Class :character  
##  Mode  :character   Median :   53.0   Mode  :character   Mode  :character  
##                     Mean   :  232.7                                        
##                     3rd Qu.:  132.0                                        
##                     Max.   :66803.0                                        
##                     NA's   :12

Think a bit on what these summaries mean. Use the space below to add some more relevant non-visual exploratory information you think helps you understand this data:

Total Employees by State

inc %>%
  group_by(State) %>%
  summarise(total_emp = sum(Employees)) %>%
  arrange(desc(total_emp)) %>%
  datatable()

Total Revenue by State

inc %>%
  group_by(State) %>%
  summarise(total_revenue = sum(Revenue)) %>%
  arrange(desc(total_revenue)) %>%
  datatable()

Average Growth Rate by Industry

inc %>%
  group_by(Industry) %>%
  summarise(sum_growth = mean(Growth_Rate)) %>%
  arrange(desc(sum_growth)) %>%
  datatable()

Question 1

Create a graph that shows the distribution of companies in the dataset by State (ie how many are in each state). There are a lot of States, so consider which axis you should use. This visualization is ultimately going to be consumed on a ‘portrait’ oriented screen (ie taller than wide), which should further guide your layout choices.

inc_state <- inc %>% 
  group_by(State) %>%
  summarise(number_of_companies = n()) %>%
  arrange(desc(number_of_companies)) 
ggplot(inc_state,aes(x = reorder(State, number_of_companies), y = number_of_companies)) + geom_bar(stat="identity") + coord_flip() + labs(title="Fastest Growing Companies")

Quesiton 2

Lets dig in on the state with the 3rd most companies in the data set. Imagine you work for the state and are interested in how many people are employed by companies in different industries. Create a plot that shows the average and/or median employment by industry for companies in this state (only use cases with full data, use R’s complete.cases() function.) In addition to this, your graph should show how variable the ranges are, and you should deal with outliers.

third_state <- inc %>% 
  group_by(State) %>%
  summarise(number_of_companies = n()) %>%
  arrange(desc(number_of_companies)) %>%
  summarise(value = nth(State, 3))

inc[complete.cases(inc), ] %>% 
  filter(State == third_state[[1, 1]]) %>%
ggplot(aes(Industry, Employees)) + 
  geom_boxplot() + 
  coord_flip() + 
  labs(title ="New York Employment Overview by Industry") +
  geom_boxplot(outlier.shape=NA) +
  scale_y_continuous(limits = quantile(inc[complete.cases(inc), ]$Employees, c(0.1, 0.9)))
## Warning: Removed 68 rows containing non-finite values (stat_boxplot).

## Warning: Removed 68 rows containing non-finite values (stat_boxplot).

Question 3

Now imagine you work for an investor and want to see which industries generate the most revenue per employee. Create a chart that makes this information clear. Once again, the distribution per industry should be shown.

inc[is.na(inc)] <- 0 
emp_rev <- inc %>% 
  group_by(Industry) %>%
  summarise(emp_revenue = sum(Revenue)/sum(Employees)) %>%
  arrange(desc(emp_revenue)) 

ggplot(emp_rev,aes(x = reorder(Industry, emp_revenue), y = emp_revenue,fill=emp_revenue)) + geom_bar(stat="identity") + coord_flip()  + labs(title ="Employee REvenue by Industry", ylab="Industry",xlab="Employee Revenue")